Methodology

European Economic Area (EEA) developers

The Solar API computes how much sunlight hits your roof in a year. It takes into account:

  • Google's database of imagery and maps
  • 3D modeling of a given roof
  • Shadows cast by nearby structures and trees
  • The sun's position over the course of a year
  • Historical cloud and temperature patterns that might affect solar energy production

The Solar API estimates values for the percent of electricity exported to the grid using data from the National Renewable Energy Laboratory (NREL). The estimates are based on the relationship between the total solar electricity production and the total amount of electricity consumed by the household. The larger the solar installation is relative to the household electricity consumption, the higher the estimate of solar electricity exported to the grid.

The Solar API also calculates potential savings in utility costs as a result of installing rooftop solar. Actual savings can vary from projected savings for a variety of reasons:

  • Fast-growing trees can shade solar installations, reducing production over time.
  • Utilities can change how much they charge their customers for electricity, affecting savings from solar.
  • Policies that are beneficial to solar installations may change (for example, net metering).
  • For states without net metering, savings may also vary by the amount of solar electricity consumed in the household compared to the amount exported to the grid.

Data sources

Building insights

To calculate solar energy production and projected savings, the Solar API uses the following data sources:

  • Imagery, 3D modeling, and shade calculations using Google's machine learning algorithms.

  • Weather data from NREL and Meteonorm. Sometimes sharp transitions between nearby stations are reflected in the map.

  • Utility electricity rates information from Clean Power Research.

  • Aggregated and anonymized solar pricing data from EnergySage and OpenSolar.

  • Solar incentives data from the following:

  • Solar Renewable Energy Credit (SREC) data from Bloomberg New Energy Finance, SRECTrade, and relevant state authorities.

Solar energy production estimates depend on many factors, such as shading, typical weather in a given area and equipment used. Additionally, Solar API mapping data may be from a different period in time than other estimates, and thus may not show recent growth or removal of trees.

Data layers

GeoTIFFs returned by the dataLayers endpoint are generated using weather data, satellite imagery, and aerial imagery from a variety of sources. Data layers GeoTIFFs are orthorectified to remove perspective distortions. For more information about available layers, see About GeoTIFF files.

Solar potential estimates

Technical potential includes electricity generated by the rooftop area suitable for solar panels, assuming supply chain disruptions and grid integration are not constraints.

There are many definitions of technical potential; other definitions may affect results by 25% or more. Based on the Solar API's definition of technical potential, installations meet the following criteria:

  • Sunlight: Every included panel receives at least 75% of the maximum annual sun in the county.
  • Installation size: Every included roof has a total potential installation size of at least 1.6 kW.
  • Space and obstacles: Any segment that has at least 4 square meters of space is considered.

The Solar API's model makes the following assumptions:

  • Each panel is assumed to be 400 W with an efficiency of 20.4%, a DC to AC derate factor of 85%, and industry-standard assumptions about other factors.
  • Panels are assumed to be mounted flush with the roof, including on flat surfaces.
  • Arrays are between 2 kW and 1000 kW. Only arrays on buildings are considered, not other spaces such as parking lots or fields.

Because we are continuously improving the model, estimates are subject to change.